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Principal Component Analysis

Editor

Listed:
  • Parinya Sanguansat

Abstract

This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis. In this book, the reader will find the applications of PCA in fields such as image processing, biometric, face recognition and speech processing. It also includes the core concepts and the state-of-the-art methods in data analysis and feature extraction.

Individual chapters are listed in the "Chapters" tab

Suggested Citation

  • Parinya Sanguansat (ed.), 2012. "Principal Component Analysis," Books, IntechOpen, number 1825, January-J.
    • Handle: RePEc:ito:pbooks:1825
    DOI: 10.5772/2340
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    File URL: https://www.intechopen.com/books/1825
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    Cited by:

    1. Bo Jiang & Zhening Li & Shuzhong Zhang, 2014. "Approximation methods for complex polynomial optimization," Computational Optimization and Applications, Springer, vol. 59(1), pages 219-248, October.
    2. Houessou, Sandrine O. & Dossa, Luc Hippolyte & Diogo, Rodrigue V.C. & Houinato, Marcel & Buerkert, Andreas & Schlecht, Eva, 2019. "Change and continuity in traditional cattle farming systems of West African Coast countries: A case study from Benin," Agricultural Systems, Elsevier, vol. 168(C), pages 112-122.

    Book Chapters

    The following chapters of this book are listed in IDEAS

    More about this item

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

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